{"id":432091,"date":"2018-06-17T23:31:17","date_gmt":"2018-06-17T23:31:17","guid":{"rendered":"https:\/\/essaypaper.org\/?p=26449"},"modified":"2018-10-24T09:13:58","modified_gmt":"2018-10-24T09:13:58","slug":"spss-program-social-movements","status":"publish","type":"post","link":"https:\/\/www.benedictsol.com\/blogs\/spss-program-social-movements\/","title":{"rendered":"SPSS program social movements"},"content":{"rendered":"<p>This week you will perform a basic linear regression. * Please be aware of the very strict data requirement for running linear regression: your DV and IV both have to be continuous variables. (Most variables at interval\/ratio level are continuous variables.) This rule is solid for DV: if your DV is a nominal or ordinal variable, you CANNOT use it as the DV for regression, not even when it is converted to a dummy variable (b\/c the regression is no longer linear). It is a necessity that your DV is a &#8220;continuous&#8221; variable with interval\/ratio level of measurement.<\/p>\n<p>If your current DV won&#8217;t work for regression test, please choose a &#8220;continuous&#8221; variable from GSS data set as your temporary DV for the week in order to practice regression analysis. Some examples of &#8220;continuous&#8221; variables from GSS 2012 data: tvhours, hrs1, etc. You don&#8217;t have to include regression test in your final portfolio if your DV won&#8217;t work for regressing test. Keep in mind, regression is also a form of significance test. Your porfolio only needs ONE significance test (we have learned: independent sample t-test, dependent sample t-test, Chi-square, and regression.)<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Creating dummy variables<\/strong><\/p>\n<p>If an IV is not continuous (like race, sex), you could make things work by creating dummy variables based on these variables. For example, based on variable &#8220;sex,&#8221; we can make a &#8220;male dummy variable&#8221; or a &#8220;female dummy variable.&#8221; Based on variable &#8220;race,&#8221; we can create a &#8220;white dummy variable&#8221; or &#8220;black dummy variable,&#8221; or &#8220;other dummy variable.&#8221;<\/p>\n<p>By custom, we&#8217;ll name the dummy variable using the value we coded as 1. For example, if we denote &#8220;male&#8221; as 1, female as 0, we&#8217;ll name this dummy as &#8220;male dummy variable.&#8221; If we denote &#8220;white&#8221; as 1, then we&#8217;ll name this dummy as &#8220;white dummy variable.&#8221; This naming method helps readers\/researchers remember\/understand what dummy variables stand for in a study.<\/p>\n<p>&nbsp;<\/p>\n<p>Here is a youtube video which shows the essential steps of creating dummy variables: https:\/\/www.youtube.com\/watch?v=R0qc4rzr9ik<\/p>\n<p>In this week&#8217;s forum discussion, you are required to run a linear regression using:<\/p>\n<ol>\n<li>your DV (if your DV is not a continuous variable, pick one from the GSS 2012 data set as your temporary DV for the week so you can practice regression)<\/li>\n<li>and two dummy variables created based on variable &#8220;sex&#8221; and &#8220;race&#8221; in the GSS 2012 data set.<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<p><strong>SPSS command to run linear regression <\/strong><\/p>\n<p><strong><em>Analyze &#8211; Regression &#8211; Linear<\/em><\/strong><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Output interpretation<\/strong><\/p>\n<p>The proper way to interpret linear regression is writing the regression equation. Here is an example:<\/p>\n<p>DV: educ (highest year of school completed, a continuous variable at I\/R level)<\/p>\n<p>IV: male dummy variable (based on variable &#8220;sex&#8221;) and white dummy variable (based on variable &#8220;race&#8221;)<\/p>\n<p>See equation below. We use * to mark the variable that is statistically significant.<\/p>\n<p>Educ=13.031-.054male+.696white*<\/p>\n<p>Here is the fun part: prediction of respondents&#8217; highest year of school completed based on their race and sex.<\/p>\n<p>Based on this equation:<\/p>\n<p>A white male by average will have 13.673 years of education: 13.031-.054*1+.696*1=13.673<\/p>\n<p>A nonwhite female by average will have 13.031 years of education: 13.031-.054*0+.696*0=13.031<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This week you will perform a basic linear regression. * Please be aware of the very strict data requirement for running linear regression: your DV and IV both have to be continuous variables. (Most variables at interval\/ratio level are continuous <a href=\"https:\/\/www.benedictsol.com\/blogs\/spss-program-social-movements\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15],"tags":[],"class_list":["post-432091","post","type-post","status-publish","format-standard","hentry","category-essay-paper-writing"],"_links":{"self":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts\/432091","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/comments?post=432091"}],"version-history":[{"count":0,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts\/432091\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/media?parent=432091"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/categories?post=432091"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/tags?post=432091"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}